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Trustworthy Semantic Web Confidentiality, Privacy and TrustOutline of the UnitLogic and InferenceWhy Rules?Example RulesMonotonic RulesLogic ProgrammingNonmonotonic RulesRule MarkupPolicies in RuleMLExample PoliciesPrivacy PoliciesSystem Architecture for Access ControlRuleML Data ManagementInference/Privacy ControlSummary and DirectionsCPT: Confidentiality, Privacy and TrustWhat is PrivacySome Privacy concernsData Mining as a Threat to PrivacySome Privacy Problems and Potential SolutionsPrivacy Constraint /Policy ProcessingSlide 23Semantic Model for Privacy ControlPrivacy Preserving Data MiningPlatform for Privacy Preferences (P3P): What is it?Platform for Privacy Preferences (P3P): OrganizationsPlatform for Privacy Preferences (P3P): SpecificationsP3P and Legal IssuesPrivacy for Assured Information SharingKey PointsApplication Specific Privacy?Data Mining and Privacy: Friends or Foes?Popular Social NetworksSocial Networks: More formal definitionSocial Network ExamplesHistorySocial Network Analysis of 9/11 Terrorists (www.orgnet.com)Social Network Analysis of 9/11 TerroristsSlide 40Slide 41Slide 42Slide 43Social Network Analysis of Steroid Usage in Baseball (www.orgnet.com)Knowledge Sharing in Organizations: Finding ExpertsKnowledge Sharing Network: Finding Experts (www.orgnet.com)Social Networks: Security and Privacy Issues: European Network and Information Security AgencySocial Networks: Security and Privacy Issues: Microsoft Recommendations http://www.microsoft.com/protect/yourself/personal/communities.mspxRole of Semantic WebFOAF: http://www.foaf-project.org/about http://en.wikipedia.org/wiki/FOAF_(software)Slide 51Slide 52Trustworthy Semantic Web Confidentiality, Privacy and TrustProf. Bhavani ThuraisinghamThe University of Texas at DallasFebruary 2010Outline of the UnitWhat are logic and inference rulesWhy do we need rules?Example rulesLogic programsMonotonic and Nonmonotoic rulesRule MarkupExample Rule Markup in XMLPolicy SpecificationRelationship to the Inference and Privacy problemsSummary and DirectionsConfidentiality, Privacy and TrustLogic and Inference First order predicate logicHigh level language to express knowledgeWell understood semanticsLogical consequence - inferenceProof systems existSound and completeOWL is based on a subset of logic – descriptive logicWhy Rules?RDF is built on XML and OWL is built on RDFWe can express subclass relationships in RDF; additional relationships can be expressed in OWLHowever reasoning power is still limited in OWLTherefore the need for rules and subsequently a markup language for rules so that machines can understandExample RulesStudies(X,Y), Lives(X,Z), Loc(Y,U), Loc(Z,U)  HomeStudent(X)i.e. if John Studies at UTDallas and John is lives on Campbell Road and the location of Campbell Road and UTDallas are Richardson then John is a Home studentNote thatPerson (X)  Man(X) or Woman(X) is not a rule in predicate logicThat is if X is a person then X is either a man of a woman. This can be expressed in OWLHowever we can have a rule of the formPerson(X) and Not Man(X)  Woman(X)Monotonic Rules Mother(X,Y)Mother(X,Y)  Parent(X,Y)If Mary is the mother of John, then Mary is the parent of JohnSyntax: Facts and RulesRule is of the form:B1, B2, ---- Bn  AThat is, if B1, B2, ---Bn hold then A holdsLogic ProgrammingDeductive logic programming is in general based on deduction-i.e., Deduce data from existing data and rules-e.g., Father of a father is a grandfather, John is the father of Peter and Peter is the father of James and therefore John is the grandfather of JamesInductive logic programming deduces rules from the data-e.g., John is the father of Peter, Peter is the father of James, John is the grandfather of James, James is the father of Robert, Peter is the grandfather of Robert-From the above data, deduce that the father of a father is a grandfatherPopular in Europe and JapanNonmonotonic RulesIf we have X and NOT X, we do not treat them as inconsistent as in the case of monotonic reasoning.For example, consider the example of an apartment that is acceptable to John. That is, in general John is prepared to rent an apartment unless the apartment ahs less than two bedrooms, is does not allow pets etc. This can be expressed as follows: Acceptable(X)Bedroom(X,Y), Y<2  NOT Acceptable(X)NOT Pets(X)  NOT Acceptable(X)Note that there could be a contradiction. But with nonmotonic reasoning this is allowed.Rule MarkupThe various components of logic are expressed in the Rule Markup Language – RuleMLBoth monotonic and nonmonotnic rules can be representedExample representation of Fact P(a) - a is a parent<fact><atom> <predicate>p</predicate> <term> <const>a</const> <term> <atom> </fact>Policies in RuleML<fact><atom> <predicate>p</predicate> <term> <const>a</const> <term> <atom>Level = L </fact>Example Policies Temporal Access Control-After 1/1/05, only doctors have access to medical recordsRole-based Access Control-Manager has access to salary information-Project leader has access to project budgets, but he does not have access to salary information-What happens is the manager is also the project leader?Positive and Negative Authorizations-John has write access to EMP-John does not have read access to DEPT-John does not have write access to Salary attribute in EMP-How are conflicts resolved?Privacy PoliciesPrivacy constraints processing-Simple Constraint: an attribute of a document is private-Content-based constraint: If document contains information about X, then it is private-Association-based Constraint: Two or more documents taken together is private; individually each document is public-Release constraint: After X is released Y becomes privateAugment a database system with a privacy controller for constraint processingSystem Architecture for Access ControlUserPull/QueryPush/resultRuleML DataDocumentsRuleML-AccessRuleMF-AdminAdmin ToolsPolicybaseCredentialbaseRuleML Data ManagementData is presented as RuleML documentsQuery language – Logic programming based?Policies in RuleMLReasoning engine-Use the one developed for RuleMLInference/Privacy ControlPoliciesOntologies RulesRule-based Data ManagementRules DataInference Engine/Rules ProcessorInterface to the Semantic WebTechnologyBy UTDSummary and


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